Initiate analytics projects​that actually create business value

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What's different about Big Data projects

Understanding how Big Data projects differ from traditional IT projects is essential to ensuring they deliver business value.

The 'secret' that vendors don't share

Big Data projects are structurally different from traditional enterprise IT applications (something IT vendors would prefer executives not to know). The massive, multi-million ERP and CRM implementations of the past had specifications that were often outdated before they were finished, and mostly overran timescales and budgets. Their expensive proprietary software - with a multitude of options and add-ons - required armies of developers and consultants.​Big Data projects are intrinsically different (although many large vendors would prefer otherwise) offering user organisations a lower cost, more flexible alternative. Here's why...

1. Open Source

First, the core analytics technologies are Open Source, not proprietary. While this does not make the infrastructure "free", it does mean that acquisition costs are substantially reduced, although the tools and integration around the core now represent a larger share of total cost.

2. SaaS

Second, the advent of SaaS (Software-as-a-Service) and cloud platforms means that even enterprise-scale analytics applications can now be rented by the month, enabling business users to themselves sign up to services at lower cost - with zero capital investment and no waiting for the IT department. In addition, the cloud model gives far more scope for variable capacity.

3. 'Experimentation with business purpose'

Third - analytics is about experiment and discovery (of unseen patterns in data, for example) - based on a cycle of continuous improvement. This enables a greater number of "what-ifs" - encouraging business people to think more proactively about different outcomes, and what factors drive them.​The incremental approach this necessitates is far less costly and risky than specifying final functionality and capacity at the outset, as in traditional enterprise projects.

Framing projects for Business Value and ROI

Depending on whether you are starting out in analytics or scaling up, to get the most from their big data projects, organisations should consider the following:

What data? Explore all available data with a focus toward business actions and outcomes that can be differentiating in the market.

How Big? Start small then grow. Focus resources around proving value quickly in one area of the business first via a pilot program or proof of value. Build internal consensus and then grow big data programs organically.

What skills? In addition to staffing up when possible, build skills of existing employees with training and development and tap outside expertise. Train business people in Data Articulacy as well as existing analysts and BI staff.

What's should the outcome be? Be clear on what success looks like: is it a dashboard that enables managers to make better decisions faster? Or is it a set of automated actions triggered by the analysis?